Upload 903_159_651_252.py

903_159_651_252.py

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+# -*- coding: utf-8 -*-
+"""903.159.651.252
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1UBJL9vF_K8ZO_vRZkvTES3G8LBRGjzGP
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+plt.style.use('seaborn-darkgrid')
+
+dataset_path = '/content/real_estate_texas_500_2024.csv'
+df = pd.read_csv(dataset_path)
+
+df.head()
+
+df['listPrice'] = df['listPrice'].fillna(df['listPrice'].mean())
+
+df.drop(columns=['baths_full_calc'], inplace=True)
+
+df.dropna(subset=['text'], inplace=True)
+
+df.info()
+
+plt.figure(figsize=(10, 6))
+sns.histplot(df['listPrice'], bins=30, kde=True)
+plt.title('Distribution of Listing Prices')
+plt.xlabel('Listing Price ($)')
+plt.ylabel('Frequency')
+plt.show()
+
+price_summary = df['listPrice'].describe()
+
+price_summary_df = pd.DataFrame(price_summary).transpose()
+
+price_summary_df
+
+plt.figure(figsize=(10, 6))
+sns.countplot(y = 'type', data=df, palette='Set2')
+plt.title('Count of Property Types')
+plt.xlabel('Count')
+plt.ylabel('Property Type')
+plt.show()
+
+type_counts = df['type'].value_counts().reset_index()
+
+type_counts.columns = ['Property Type', 'Count']
+
+type_counts
+
+type_counts = df['type'].value_counts().reset_index()
+
+type_counts.columns = ['Property Type', 'Count']
+
+type_counts
+
+yearly_summary = df.groupby('year_built').agg(
+    Average_Listing_Price=('listPrice', 'mean'),
+    Average_Square_Footage=('sqft', 'mean')
+).reset_index()
+
+yearly_summary['Average_Listing_Price'] = yearly_summary['Average_Listing_Price'].round(2)
+yearly_summary['Average_Square_Footage'] = yearly_summary['Average_Square_Footage'].round(2)
+
+yearly_summay = yearly_summary.sort_values(by='year_built')
+
+yearly_summary
+
+yearly_summary = df.groupby('year_built').agg(
+    Average_Listing_Price=('listPrice', 'mean'),
+    Average_Square_Footage=('sqft', 'mean')
+).reset_index()
+
+yearly_summary['Average_Listing_Price'] = yearly_summary['Average_Listing_Price'].round(2)
+yearly_summary['Average_Square_Footage'] = yearly_summary['Average_Square_Footage'].round(2)
+
+yearly_summary = yearly_summary.sort_values(by='year_built')
+
+yearly_summary
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.decomposition import LatentDirichletAllocation
+
+vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')
+dtm = vectorizer.fit_transform(df['text'])
+
+lda = LatentDirichletAllocation(n_components=5, random_state=42)
+lda.fit(dtm)
+
+def display_topics(model, feature_names, no_top_words):
+  for topic_idx, topic in enumerate(model.components_):
+    print(f"Theme {topic_idx+1}:")
+    print(" ".join([feature_names[i] for i in topic.argsort()[:-no_top_words -1:-1]]))
+    print()
+
+display_topics(lda, vectorizer.get_feature_names_out(), 10)
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set(style="whitegrid")
+
+fig, axes = plt.subplots(2, 2, figsize=(16, 12))
+fig.suptitle('Texas Real Estate Market Insights - 2024', fontsize=20)
+
+sns.histplot(df['listPrice'], kde=True, ax=axes[0,0], color='skyblue')
+axes[0,0].set_title('Distribution of Listing Prices')
+axes[0, 0].set_xlabel('Listing Price ($)')
+axes[0, 0].set_ylabel('Frequency')
+
+avg_price_by_type = df.groupby('type')['listPrice'].mean().sort_values()
+avg_price_by_type.plot(kind='barh', ax=axes[0,1], color='lightgreen')
+axes[0, 1].set_title('Average Listing Price by Property Type')
+axes[0, 1].set_xlabel('Average Listing Price ($)')
+axes[0, 1].set_ylabel('Property Type')
+
+properties_by_year = df.groupby('year_built').size()
+properties_by_year.plot(ax=axes[1, 0], color='salmon')
+axes[1, 0].set_title('Count of Properties by Year Built')
+axes[1, 0].set_xlabel('Year Built')
+axes[1, 0].set_ylabel('Count')
+
+plt.tight_layout(rect=[0, 0.03, 1, 0.95])
+
+fig.delaxes(axes[1,1])
+
+plt.show()